Scikit-learn style model finetuning for NLP

Finetune ships with pre-trained language models from "Improving Language Understanding by Generative Pre-Training" (GPT) and "Language Models are Unsupervised Multitask Learners" (GPT-2). Base model code has been adapated from the GPT and GPT-2 github repos.

Huge thanks to Alec Radford and Jeff Wu for their hard work and quality research.

Finetune Quickstart Guide

Finetuning the base language model is as easy as calling Classifier.fit:

model = Classifier()               # Load base model
model.fit(trainX, trainY)          # Finetune base model on custom data
predictions = model.predict(testX) # [{'class_1': 0.23, 'class_2': 0.54, ..}, ..]
model.save(path)                   # Serialize the model to disk

Reload saved models from disk by using LanguageModelClassifier.load:

model = Classifier.load(path)
predictions = model.predict(testX)

If you have large amounts of unlabeled training data and only a small amount of labeled training data, you can finetune in two steps for best performance.

model = Classifier()               # Load base model
model.fit(unlabeledX)              # Finetune base model on unlabeled training data
model.fit(trainX, trainY)          # Continue finetuning with a smaller amount of labeled data
predictions = model.predict(testX) # [{'class_1': 0.23, 'class_2': 0.54, ..}, ..]
model.save(path)                   # Serialize the model to disk

Documentation

Full documentation and an API Reference for finetune is available at finetune.indico.io.

Installation

Finetune can be installed directly from PyPI by using pip

pip3 install finetune

or installed directly from source:

git clone -b master https://github.com/IndicoDataSolutions/finetune && cd finetune
python3 setup.py develop              # symlinks the git directory to your python path
pip3 install tensorflow-gpu --upgrade # or tensorflow-cpu
python3 -m spacy download en          # download spacy tokenizer

In order to run finetune on your host, you'll need a working copy of CUDA >= 8.0, libcudnn >= 6, tensorflow-gpu >= 1.6 and up to date nvidia-driver versions.

You can optionally run the provided test suite to ensure installation completed successfully.

pip3 install pytest
pytest

Docker

If you'd prefer you can also run finetune in a docker container. The bash scripts provided assume you have a functional install of docker and nvidia-docker.

git clone https://github.com/IndicoDataSolutions/finetune && cd finetune

# For usage with NVIDIA GPUs
./docker/build_gpu_docker.sh      # builds a docker image
./docker/start_gpu_docker.sh      # starts a docker container in the background, forwards $PWD to /finetune

docker exec -it finetune bash # starts a bash session in the docker container

For CPU-only usage:

./docker/build_cpu_docker.sh
./docker/start_cpu_docker.sh

Model Types

finetune ships with a half dozen different classes for finetuning the base language model on different task types.

• Classifier
• Regressor
• SequenceLabeler
• MultiFieldClassifier
• MultiFieldRegressor
• MultiLabelClassifier
• Comparison
• OrdinalRegressor
• ComparisonOrdinalRegressor
• MultiTask

For example usage of each of these model types, see the finetune/datasets directory. For purposes of simplicity and runtime these examples use smaller versions of the published datasets.